Systems and methods for intelligent discard in a communication network

ABSTRACT

Systems and methods for optimizing system performance of capacity and spectrum constrained, multiple-access communication systems by selectively discarding packets are provided. The systems and methods provided herein can drive changes in the communication system using control responses. One such control responses includes the optimal discard (also referred to herein as “intelligent discard”) of network packets under capacity constrained conditions. Some embodiments provide an interactive response by selectively discarding packets to enhance perceived and actual system throughput, other embodiments provide a reactive response by selectively discarding data packets based on their relative impact to service quality to mitigate oversubscription, others provide a proactive response by discarding packets based on predicted oversubscription, and others provide a combination thereof

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/813,856, titled “Systems and Methods for Intelligent Discard in aCommunication Network,” filed Jun. 11, 2010, which claims the benefit ofU.S. provisional patent application Ser. No. 61/186,707 entitled “Systemand Method for Interactive Intelligent Discard in a CommunicationNetwork,” filed on Jun. 12, 2009, U.S. provisional patent applicationSer. No. 61/187,113 entitled “System and Method for InteractiveIntelligent Discard in a Communication Network,” filed on Jun. 15, 2009,and U.S. provisional patent application Ser. No. 61/187,118 entitled“System And Method For Proactive Intelligent Discard In A CommunicationNetwork,” filed on Jun. 15, 2009, all of which are incorporated hereinby reference in their entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of communicationsystems and more specifically to systems and methods for optimizingsystem performance by selectively discarding packets in capacity andspectrum constrained, multiple-access communication systems.

BACKGROUND

In capacity and spectrum constrained, multiple-access communicationsystem, two goals are omnipresent: the successful transfer ofinformation, and the minimization of such transmissions from disruptingother transfers. Often these goals are in conflict with each other, andthus represent opportunity for system optimization.

In a cellular network, for example, the creation of a positive userexperience is the success criteria for the transport of information.Often this metric is further defined as the quality of service of aparticular user task or application. In contrast, this activity can beviewed by its effect on other network users, specifically through theusage of limited system resources and through the creation of channelinterference.

SUMMARY

Systems and methods for optimizing system performance of capacity andspectrum constrained, multiple-access communication systems byselectively discarding packets are provided. The systems and methodsprovided herein can drive changes in the communication system usingcontrol responses. One such control response includes the optimaldiscard (also referred to herein as “intelligent discard”) of networkpackets under capacity constrained conditions. Some embodiments providean interactive response by selectively discarding packets to enhanceperceived and actual system throughput. Other embodiments provide areactive response by selectively discarding data packets based on theirrelative impact to service quality to mitigate oversubscription wherethe demand for bandwidth exceeds the available bandwidth. Additionalembodiments provide a proactive response by discarding packets based onpredicted oversubscription. Other embodiments provide a combination ofthese techniques to decrease oversubscription.

According to another embodiment, a multivariate control system formitigating the effects of various interference scenarios in a capacityand spectrum constrained, multiple-access communication network isprovided. The control system includes a policy parameters module, anenvironment parameters module, a control set points module, a real-timeprofile module, an assessment module, and a control response module. Thepolicy parameters module is configured to receive policy parameters thatdefine operational requirements for the communication network. Theenvironment parameters module is configured to receive environmentparameters that represent real-time information describing the operatingstatus of the communication network. The control set points module is incommunication with the policy parameters module and is configured toreceive policy parameters from the policy parameters module and togenerate a set of control set points that can be used to assess whethercurrent operating status of the communication network meets theoperational requirements defined in the policy parameters. The real-timeprofile module in communication with the environment parameters moduleand is configured to receive environment parameters from the environmentparameters module and to generate a real-time profile of thecommunication network that represents current operating conditions ofthe communication network. The assessment module is in communicationwith the real-time profile module and the control set point module andis configured to receive the real-time profile from the real-timeprofile module and the set of control set points from the control setpoint module, to determine whether the current operating conditions ofthe communication network meet the operational requirements in thepolicy parameters, and to generate feedback signals indicating that thecurrent operating conditions of the communication network do not meetthe operational requirement. The control response module is incommunication with the assessment module and is configured to receivefeedback signals from the assessment module and to generate controlsignals for one or more components of the communication system to adjustthe operating parameters of the one or more components of thecommunication system.

According to an embodiment, a method for mitigating the effects ofvarious interference scenarios in a capacity and spectrum constrained,multiple-access communication network is provided. The method includesobtaining environment inputs comprising real-time information describingthe operating status of the communication network, deriving a real-timeprofile of the network based on the environment inputs, the real-timeprofile representing current operating conditions of the communicationnetwork, determining whether the real-time profile satisfies a set ofcontrol set points, the control set points representing operationalrequirements for the communication network based on policy parameters,generating a feedback adjustment signal if the real-time profile doesnot satisfy at least one control set point, generating control signalsbased on the feedback adjustment signal for one or more components ofthe communication system to adjust the operating parameters of the oneor more components of the communication system.

Other features and advantages of the present invention should beapparent from the following description which illustrates, by way ofexample, aspects of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The details of the present invention, both as to its structure andoperation, may be gleaned in part by study of the accompanying drawings,in which like reference numerals refer to like parts, and in which:

FIG. 1 is a block diagram of a wireless communication network in whichthe systems and methods disclosed herein can be implemented according toan embodiment;

FIG. 2A is block diagram of another wireless communication network inwhich the systems and methods disclosed herein can be implementedaccording to an embodiment;

FIG. 2B is a block diagram of an access point or base station that canbe used to implement the systems and methods illustrated in FIGS. 3-6according to an embodiment.

FIG. 3 is a logical block diagram of a system for mitigating effects ofinterference scenarios in a wireless communication network according toan embodiment;

FIG. 4 is a flow diagram of a method that can be used to generate thefeedforward and feedback adjustments of the radio frequency (RF) networkand system environment using the system illustrated in FIG. 3 accordingto an embodiment;

FIG. 5 is a flow diagram of a method for mitigating effects ofinterference scenarios in a wireless communication network according toan embodiment; and

FIG. 6 is a flow diagram of a method for mitigating effects ofinterference scenarios in a wireless communication network according toan embodiment.

DETAILED DESCRIPTION

Some embodiments provide systems and methods for a multivariate controlsystem that can be implemented in a base station. The control system canbe configured to for mitigating the effects of various interferencescenarios in a capacity and spectrum constrained, multiple-accesscommunication network. In other embodiments, the control system can beconfigured for making adjustments to or changing the overall bandwidthdemands. The systems and methods provided herein can drive changes inthe communication system using control responses. One such controlresponses includes the optimal discard (also referred to herein as“intelligent discard”) of network packets under capacity constrainedconditions. Some embodiments provide an interactive response byselectively discarding packets to enhance perceived and actual systemthroughput, other embodiments provide a reactive response by selectivelydiscarding data packets based on their relative impact to servicequality to mitigate oversubscription, others provide a proactiveresponse by discarding packets based on predicted oversubscription, andothers provide a combination thereof.

According to an embodiment, an interactive response technique isprovided that allows transmission and radio access network (RAN)/radiofrequency (RF) parameters to be optimized for robustness againstinterference from neighboring cells and optimized for mitigation ofinterference to neighboring cells. These optimizations are performed bydetermining and considering throughput levels and associated qualityscores for a set of active services. A high quality user experience canbe maintained where perceived and actual system throughput is controlledby selectively discarding packets.

According to an embodiment, a reactive response technique is providedthat allows selected data packets to be discarded based on theirrelative impact to service quality in order to mitigate oversubscriptioncaused by modification of transmission parameters or by varying theRAN/RF parameters to mitigate interference between neighboring cells.Reactively discarding packets in reaction to varying available bandwidthcan provide an increase in perceived quality of the user experience fora given amount of bandwidth and can provide an increase in the number ofservices that can be maintained for a given amount of bandwidth.

According to an embodiment, a proactive response technique is providedthat can improve the quality of the user experience and systemthroughput by predicting oversubscription and selectively discardingpackets or marking packets for efficient discard prior to anticipatedoversubscription. Proactively discarding packets in reaction toanticipated oversubscription can provide an increase in perceivedquality of the user experience for a given amount of bandwidth and canprovide an increase in the number of services that can be maintained fora given amount of bandwidth and for a given amount of change inbandwidth. In an embodiment, selectively proactively discarding packetscan be used to optimize transmission and RAN/RF parameters to increaserobustness against interference from neighboring cells and to mitigateinterference to neighboring cells in anticipation of events which causea need for such parameter changes. Proactively applying intelligentdiscard and considering intelligent discard to proactively modifytransmission and RAN/RF parameters before a bandwidth limiting eventoccurs can provide a better user experience transition than can beachieved by waiting to apply intelligent discard and to modifytransmission and RAN/RF parameters until after such a bandwidth limitingevent.

Some embodiments provide systems and methods for a multivariate controlsystem that can be implemented in a base station. The control system canbe configured to mitigate the effects of various interference scenariosin a capacity and spectrum constrained, multiple-access communicationnetwork. In other embodiments, the control system can be configured formaking adjustments to or changing the overall bandwidth demands.

The systems and methods disclosed herein can be applied to variouscapacity-limited communication systems, including but not limited towireline and wireless technologies. For example, the systems and methodsdisclosed herein can be used with Cellular 2G, 3G, 4G (including LongTerm Evolution (“LTE”), LTE Advanced, WiMax), WiFi, Ultra MobileBroadband (“UMB”), cable modem, and other wireline or wirelesstechnologies. Although the phrases and terms used herein to describespecific embodiments can be applied to a particular technology orstandard, the systems and methods described herein are not limited tothe these specific standards.

Although the phrases and terms used to describe specific embodiments mayapply to a particular technology or standard, the methods describedremain applicable across all technologies.

According to an embodiment, the systems and methods disclosed herein,including intelligent discard of packets, can be practiced within anyentity within the communications system that performs scheduling. Thisincludes the scheduling of downlink bandwidth by any form of basestation, including macrocell, picocell, enterprise femtocell,residential femtocell, relays, or any other form of base station.According to an embodiment, intelligent discard can be performed by anyform of device which transmits in the uplink direction including userdevices, both fixed and mobile, and relay devices. According to anembodiment, intelligent discard can be performed by a schedulingalgorithm, housed in the core network which centrally directs theactions of devices. According to an embodiment, intelligent discard canbe predictively performed by an entity such as a base station thatallocates uplink bandwidth for use by another entity, such as a userdevice known to be capable of intelligent discard. The base station andthe user device can negotiate whether or not the user device hasintelligent discard capability, or in some embodiments, whether the userdevice has intelligent discard capability can be determined based on themodel identification of the user device.

Basic Deployments

FIG. 1 is a block diagram of a wireless communication network in whichthe systems and methods disclosed herein can be implemented according toan embodiment. FIG. 1 illustrates a typical basic deployment of acommunication system that includes macrocells, picocells, and enterprisefemtocells. In a typical deployment, the macrocells can transmit andreceive on one or many frequency channels that are separate from the oneor many frequency channels used by the small form factor (SFF) basestations (including picocells and enterprise or residential femtocells).In other embodiments, the macrocells and the SFF base stations can sharethe same frequency channels. Various combinations of geography andchannel availability can create a variety of interference scenarios thatcan impact the throughput of the communications system.

FIG. 1 illustrates a typical picocell and enterprise femtocelldeployment in a communications network 100. Macro base station 110 isconnected to a core network 102 through a standard backhaul 170.Subscriber stations 150(1) and 150(4) can connect to the network throughmacro base station 110. In the network configuration illustrated in FIG.1, office building 120(1) causes a coverage shadow 104. Pico station130, which can be connected to core network 102 via standard backhaul170, can provide coverage to subscriber stations 150(2) and 150(5) incoverage shadow 104.

In office building 120(2), enterprise femtocell 140 provides in-buildingcoverage to subscriber stations 150(3) and 150(6). Enterprise femtocell140 can connect to core network 102 via ISP network 101 by utilizingbroadband connection 160 provided by enterprise gateway 103.

FIG. 2A is a block diagram of another wireless communication network inwhich the system and methods disclosed herein can be implementedaccording to an embodiment. FIG. 2A illustrates a typical basicdeployment in a communications network 200 that includes macrocells andresidential femtocells deployed in a residential environment. Macrocellbase station 110 can be connected to core network 102 through standardbackhaul 170. Subscriber stations 150(1) and 150(4) can connect to thenetwork through macro base station 110. Inside residences 220,residential femtocell 240 can provide in-home coverage to subscriberstations 150(7) and 150(8). Residential femtocells 240 can connect tocore network 102 via ISP network 101 by utilizing broadband connection260 provided by cable modem or DSL modem 203.

FIG. 2B is a high level block diagram of an access point or basestation. The base station includes a modem section 272 which transmitsand receives wireless signals. The modem can also measure and determinevarious characteristics of the received signals. The control andmanagement section 270 was generally responsible for the operation ofthe base station. In some embodiments described herein, the control andmanagement section 270 implements the system and method described hereinin connection with FIGS. 3-6.

Interference Scenarios

Various interference scenarios can result in decreases in perceived andactual performance of the communications network. For example, the 3rdGeneration Partnership Project (3GPP) has identified a number ofinterference scenarios in a technical report (3GPP TR 25.967), which ishereby incorporated by reference in its entirety. Some examples ofinterference scenarios include: (1) Uplink (UL) transmission fromsubscriber station to SFF base station interfering with UL of macrocellbase station; (2) Downlink (DL) transmission of SFF base stationinterfering with macrocell base station DL; (3) UL transmission fromsubscriber station to macrocell base station interfering with SFF basestation uplink; (4) DL transmission of macro base station interferingwith SFF base station DL; (5) UL transmission from subscriber station toSFF base station interfering with UL of SFF station; (6) DL transmissionof SFF base station interfering with SFF base station DL; and (7)interference to and from systems of other technologies.

Avoidance and Mitigation Techniques

FIG. 3 is a logical block diagram illustrating an example of thefunctional elements of a multivariate control system for mitigating theeffects of various interference scenarios in a capacity and spectrumconstrained, multiple-access communication network, such as thosedescribed above, according to an embodiment. The functionality of thesystem is show in FIG. 3 broken down into modules to more clearlyillustrate the functionality of the control system. The control systemcan be implemented in a macrocell base station, picocell, or femtocell,such as macrocell base station 110, pico station 130, and residentialfemtocell 240 illustrated in FIGS. 1 and 2. Alternatively, portions canbe distributed to a base station controller (BSC) or other element ofcore network 102.

In an embodiment, the control system can be configured to provideoptimal responses in the following areas: (1) interference avoidance and(2) interference mitigation. The control system can avoid radiofrequency (RF) interface through optimal control of RF/RAN parameters.The control system can also preserve packet quality of service (“QoS”)when interference cannot be avoided or when interference avoidance ormitigation result in decreased bandwidth availability.

According to an embodiment, various types of input parameters can beused by the control system. In an embodiment, these input parameters canbe divided into policy parameters and environment parameters. Policyparameters module 310 can be configured to receive policy parameters,and environment parameter module 320 can be configured to receiveenvironment parameters. The policy parameters received by policyparameters module 310 are operational requirements defined by, forexample, the network provider. These policy parameters can be brokendown into two groups of system requirements: QoS policies andinterference policies. In an embodiment, the policy parameters caninclude QoS policies at an application level, by time/day, by servicelevel agreement (SLA), manually define QoS parameters, or a combinationthereof The policy parameters can also include policies related tovarious interference related parameters, such as received signalstrength indicator (RSSI), energy per bit to noise power spectraldensity ratio (E_(b)/N₀), carrier-to-interference ratio (C/I), noisefloor (the measure of the signal created from the sum of all of thenoise source and unwanted signals), or other interference relatedparameters. The control system can use the policy parameters todetermine the types of actions that can undertaken to avoid interferenceand to mitigate interference when interference cannot be avoided.

The environment input parameters received by environment parametermodule 320 comprise real-time information that describes the operatingstatus of the RF network and system environment. This information can beobtained at a base station (e.g., a macrocell, picocell, or femtocell asdepicted in FIGS. 1 and 2) or reported by a subscriber station and canalso include information about neighboring cells. The environment inputparameters 320 can be further divided into two categories of inputparameters: self environment parameters and remote environmentparameters. The self environment parameters are environment parametersrelated to or obtained by the station in which the control system isimplemented. For example, in one embodiment, the self environmentparameters can include Layer 1-7 parameters of both the RF and backhaulfemtocell or picocell ports. Remote environment parameters are relatedto or obtained from other cells and/or user equipment operating nearbythe base station that can have an impact on the operating environment ofthe base station. For example, in an embodiment, the remote environmentparameters can include Layer 1-7 parameters of the user equipment (UE),Core Network and other neighboring cells defined by base stations, suchas evolved Node B (eNB or eNodeB), and pico stations and femtocells,such as evolved Home Node B devices (eHNB or Home eNodeB), collectivelye(H)NB devices.

From the policy parameters and environment parameters, additional setsof parameters can be derived including control set points, real-timeprofile, and patterns. Control set points module 315 is configured toderive control set points from the policy inputs received by the policyparameters module 310 from the network provider or can be derivedmanually. The control set points comprise quantitative parameters thatcan be used as control loop target values. These quantitative parameterscan be divided into QoS parameters and interference parameters. Someexamples of QoS parameters include frame size and frame rate, and frameerror rate (FER) by packet type for video content. Some additionalexamples of QoS parameters include mean opinion score (“MOS”), latency,and jitter for voice content. Additional examples of QoS parameters arethroughput and bit error rate (BER) for data content. The interferencerelated parameters can include, but are not limited to, variousinterference related parameters, such as received signal strengthindicator (RSSI), energy per bit to noise power spectral density ratio(E_(b)/N₀), carrier-to-interference ratio (C/I), and noise floor (themeasure of the signal created from the sum of all of the noise sourceand unwanted signals). The control set points can be used by assessmentmodule 330 of the control system to assess the current state of thecommunication network based on a real-time profile 325 of the RF networkand system environment and to determine whether to feedback signalsshould be generated to adjust the operating state of the network.

The real-time profile module 325 is configured to generate a real-timeprofile of the communication system based on the environment inputparameters received by environment parameter module 320. In anembodiment, the real-time profile comprises quantitative parameters thatreflect current operating conditions of the communication network. Thereal-time profile can comprise QoS and interference related parameters.Some examples of QoS-related parameters include BER, throughput,latency/jitter, protocol-related parameters, and application-relatedparameters. The interference related parameters can include, but are notlimited to, various interference related parameters, such as receivedsignal strength indicator (RSSI), energy per bit to noise power spectraldensity ratio (E_(b)/N₀), carrier-to-interference ratio (C/I), and noisefloor (the measure of the signal created from the sum of all of thenoise source and unwanted signals). According to an embodiment, thereal-time profile can comprise a datagram, spreadsheet, or otherrepresentation of the current operating conditions of the communicationnetwork.

Patterns module 335 is configured to generate patterns that comprise aset of historical quantitative parameter patterns that can be used togenerate feedforward control responses. The patterns can be derived fromthe environment parameters received by environment parameter module 320and the real-time profile generated by real-time profile module 325.These patterns can reflect usage patterns on the network. For example,in an embodiment, the patterns can include specific drivers related tothe date and/or time, a specific application or protocol, and/or aspecific UE.

The control set points generated by control set points module 315 andthe real-time profile generated by real-time profile module 325 can beassessed by assessment module 330 to compare the current operatingparameters of the communication network represented in the real-timeprofile with the control set points to determine whether currentoperating conditions of the network meet the operational requirementsincluded in the policy parameters. If the current operating conditionsof the network do not meet the requirements set forth in the policyparameters, the assessment module 330 can generate feedback signalsindicating that operating parameters of the communication system need tobe adjusted.

The control response module 340 is configured to receive the feedbacksignals from the assessment module 330. The control response module 340(also referred to herein as an optimization module) is configured tooptimize the operating parameters of the communication network in anattempt to meet the requirements of the operator policy. The controlresponse module 340 can be configured to generate control signals basedon the feedback signals received from the assessment module 330. Thecontrol signals fall into two categories: “self” and “remote.” Selfcontrol signals can be applied to the base station itself (the e(H)NB)to change the operating parameters of the base station and remotecontrol signals can be applied to remote devices or components network,including UEs, the Core Network, and other e(H)NB to change theoperating parameters of the remote devices or components of the network.

FIG. 4 is a flow diagram of a method that can be used to generate thefeedforward and feedback adjustments of the RF network and systemenvironment using the system illustrated in FIG. 3 according to anembodiment. Updated environment inputs are obtained that represent thecurrent state or new current state of the RF network and systemenvironment (step 410). The environment inputs correspond to theenvironment parameters generated by environment parameter module 320 ofthe communication system. As described above, the environment parameterscan comprise real-time information related to the RF network and systemenvironment obtained from both the picocell or femtocell, subscriberstations, and neighboring cells including macrocells, picocells, andfemtocells. A real-time profile is also derived from the updatedenvironment inputs (step 415). In an embodiment, the real-time profilecorresponds to real-time profile generated by real-time profile module325 and can be generated from the environment input parameters obtainedin step 410.

A determination can be made whether the real-time profile matches theset points generated by control set point module 315 (step 420). Asdescribed above, the control set points comprise quantitative parametersthat can be used as control loop target values. The control set pointscan be derived from the policy parameters defined by the networkprovider. If the real-time profile does not match the set points, thereal-time information collected related to the RF network and the systemenvironment indicates that the operating state of the network hasdeviated from the set points that were derived from the networkprovider's operator policy. In response, the feedback adjustment controlsignals can be generated (step 440) to steer the communications networktoward an operating state that is consistent with the policy parameters.

Patterns module 335 can derive patterns from the real-time profile andenvironment input parameters (step 425). In an embodiment, the patternscomprise a set of historical quantitative parameter patterns. Adetermination is made whether a pattern has changed (step 430), and if apattern has changed, the historical quantitative parameter patterns thatcan be used to generate feedforward control responses (step 435) thatcan be used to adjust various operating parameters that can be used tosteer the communication network toward a desired state.

The feedback signals generated in step 440 and the feedforward signalsgenerated in step 435 can be used to generate a set of control signals(step 450) that can be applied to the ‘self’ e(H)NB and remote devices,including UEs, the Core Network and other e(H)NB.

A determination is made whether the network provider has made changes tothe operator policy (step 470). If the network operator has made changesto the policy parameters, new set points can be generated by the controlset points module 315 from the operator policy (step 475) beforereturning to step 410. Otherwise, the method returns to step 410 wherethe environment inputs are collected.

Inputs

The SFF base station can have access to various environmentalinformation that can be used in generating feedback and feedforwardsignals for the control response module 340. This information can bepart of the environment parameters 320 that can be used to generate thereal-time profile generated by real-time profile module 325 and thepatterns generated by patterns module 335. The information can becollected by the SFF base station during step 410 of the methodillustrated in FIG. 4. For example; according to an embodiment, thefollowing environmental input data is typically available (sensed,reported to, etc.) to an SFF base station: (1) signal strength frommacro BTS(s), (2) signal strength from other SFF base station(s), (3)knowledge of whether the macro base stations and the SFF base stationsare co-channel (or adjacent channel); (4) neighboring cellidentification data; and (5) macro network specific information andsystem parameter thresholds. Some examples of additional informationthat can be available to an SFF base station include: DL co-channelcarrier RSSI, DL adjacent channel carrier RSSI, common pilot channel(CPICH) Energy per Chip to Total Noise Power (Ec/No), received totalwideband power (RTWP), public land mobile network (PLMN) ID, cell ID,Local Area Code (LAC), Routing Area Code (RAC), scrambling codes,co-channel CPICH received signal code power (RSCP), adjacent channelCPICH RSCP, P-CPICH Tx Power, macro cell data rate and macro celldead-zone coverage. The macro cell data rate and macro cell dead-zonecoverage can take into account various information, including macrostation load, the number of active SFF base stations, distance of theSFF base stations to the macro station, fading environment, andtime-of-day. The SFF base station can have macro station parameterinformation available to the SFF base station, including target SNR,measured SNR, and received power.

Adjustments

The following item are some examples of the type of parameters that canbe adjusted in step 450 by an SFF base station in response to theenvironment information received via sensing: (1) DL power, (2) UL noiserise target (UL scheduler), (3) UL power, (4) control channel/datachannel power ratio, (5) receiver gain, (6) carrier frequency, (7) DLscrambling code, (8) LAC, and (9) RAC.

Additional Inputs

The SFF base station can have access to additional input information.This information can be part of the environment parameters 320 that canbe used to generate the real-time profile 325 and patterns 335. Theinformation can be collected by the SFF base station during step 410 ofthe method illustrated in FIG. 4. For example, additional inputs such asreal-time traffic metrics can also be available to an SFF base stationand can be used to generate the real time profile 325. For example,real-time traffic metrics, such as the number of active UEs, the numberof idle UEs, indicators of UE mobility and changes in position, theaggregate UL usage, the aggregate DL usage, the Layer 4-7 profile(Voice, video, web, FTP, etc.), the backhaul capacity, and the perconnection BER. The per connection BER data can be obtained beforehybrid automatic repeat request (HARQ) or other retry mechanisms orafter HARQ or other retry mechanisms. In some embodiments, theper-connection BER can be obtained without HARQ. In some embodiments,the per-connection BER data can include statistics on retries.

Historical pattern data (such as patterns 335) can also be available tothe SFF base station, such as time of day data, day of week data, localholiday data, known/unknown UE entering the network, typical usagerates, and typical usage durations. This historical data can be used togenerate patterns 335, which can be used to generate feedforward controlsignals as described above.

Policy input data can also be available to the SFF base station, such asQoS requirements data, priorities data, packet inspection data, andadvanced antenna inputs. This policy information can be part of theoperator policy data 310 described above. The QoS requirements data caninclude delay tolerance data jitter tolerance data, BER/PER tolerancedata, minimum acceptance rate data, and/or other QoS related data. Thepriority input data can include data related to priorities betweenusers, between classes of service, between connections, and/or betweenpackets from the same class of service. Packet inspection data andadvanced antenna inputs data can also be available to the SFF basestation.

Additional Parameters Adjusted

Additional parameters can be adjusted in step 450 in an attempt toremedy oversubscription. In one embodiment, RAN/RF parameters, such asmodulation and coding, subchannelization, time within frame, subchanneland time hopping, multiple-input multiple-output (MIMO) parameters, andbeamforming can be used to remedy oversubscription on the communicationsystem. In another embodiment, traffic policing can be used to remedyoversubscription. Various types of types of traffic policing can beused, including rate limiting, packet blocking, packet dropping and/orintelligent discard. Various techniques for intelligent discard that canbe used to remedy oversubscription are described below.

Optimizing Performance

According to an embodiment, the described systems and methods include anoptimization module to optimize performance by varying extended RAN/RFparameters based on QoS, priority, and policy (also referred to hereinas the “optimization module”). According to an embodiment, theoptimization module can be implemented in a base station, including amacrocell, picocell, or femtocell base station.

In one embodiment, the optimization module is configured to establishthe BER/PER or other quality metric level for each class of service(CoS) or connection. In one embodiment, the quality metric can beprioritized based on known/unknown user equipment, where known userequipment can be given priority over unknown user equipment. The userequipment can include mobile, transient, and stationary subscriberstations. In another embodiment, the quality metric can be prioritizedbased on specific UE identity, and in yet another embodiment, thequality metric can be prioritized based on the application.

According to an embodiment, the optimization module is configured toestablish required/desired throughput for each class of service orconnection. The required/desired throughput can be optionally modifiedbased on whether a UE is known or unknown, based on a specific UEidentity, or based on a specific application.

According to an embodiment, the optimization module is configured to usea standards based approach to derive baseline interference scenario andbaseline RAN/RF parameters.

According to an embodiment, the baseline interference scenario andbaseline RAN/RF parameters can change in real-time as conditions changein the communications network. For example, some of the changingconditions include the number of active/inactive UEs, traffic inneighboring cells, and indicators of change in position of UE, such asround trip delay, RSSI, and tracking via receive beamforming.

According to an embodiment, optimization module can vary the actualscenario and actual RAN/RF parameters in real time as conditions change.For example, in one embodiment, if the BER or quality metric of servicedrops below a threshold, the required physical parameters of service canbe set to be more robust than a baseline value. For example, MIMO can bechanged and beamforming advanced antenna techniques can be applied.Furthermore, modulation and coding changes can be made to improverobustness. Alternatively, a determination can be made whether to exceedbaseline interference scenarios and/or RAN/RF parameters. For example,the determination can be based on sensing data, permissionfrom/negotiation with central controller, permission from/negotiationwith neighboring BTSs, or use spatial multiplexing (beamforming, etc) tominimize interference. Alternatively, a subchannel and time location inframe (e.g., Orthogonal Frequency Division Multiplexing (OFDM) symbol,time slot, etc.) can be chosen to avoid regular interference.Alternatively, subchannels and time location in the frames can berandomized to statistically avoid interference or selectively increasepotential caused interference, but mitigate through randomization ofimpact.

In an embodiment, if demand exceeds new maximum aggregate throughput (DLor UL, including bandwidth for managing active and idles UEs) thenoptimization module can take steps to mitigate the oversubscription. Inone embodiment, delay tolerant traffic can be delayed to temporarilyreduce demand. For example, one approach includes delaying and bufferingcontent, such as a live video. Live video can be delayed and buffered solong as the variation in delay (jitter) remains within the capacity/timeconstraints of the delay/jitter buffer. In another embodiment,substantial deferral of “download for later use” content is used todecrease demand on the network. For example, in one embodiment,downloads of music and/or video content that is not being consumed asthe content is received (e.g., non-streaming content) can be temporarilydeferred until demand on the network decreases.

In another embodiment, if demand exceeds the new maximum aggregatethroughput, optimization module can selectively discard frames within aservice to reduce demand on the network. For example, some MovingPicture Experts Group (MPEG) frames are less important than others andcan be selectively discarded in order to decrease demand on thecommunication system. In another example, packets having above a minimumacceptable rate for a service can be discarded to reduce demand.

In yet another embodiment, if demand exceeds the new maximum aggregatethroughput, call admission control (CAC) can be used to curtailservices. In some embodiments, services can be curtailed based onpriority, while in some embodiments services can be curtailed based onthe application.

According to an embodiment, the various mitigating actions taken ifdemand exceeds the new maximum aggregate throughput can be reversed whenconditions improve. For example, in one embodiment, hysteresis can beused to smooth reactions.

FIG. 5 is a flow chart illustrating a method that can be implemented bythe optimization module described above to optimizing performance byvarying extended RAN/RF parameters based on QoS, priority, and policyaccording to an embodiment. In an embodiment, the method illustrated inFIG. 5 can be implemented by the control system illustrated in FIG. 3.In an embodiment, the method of FIG. 5 can be implemented in step 450 ofFIG. 4.

The method starts at step 501 where in parallel the method determinesRAN/RF aspects of the system (steps 510, 512, and 514) and QoS andtraffic management aspects of the system (steps 520, 522, 524, and 526).

In step 510, the baseline interference scenario is derived and monitoredand baseline for RAF/RF parameter settings is created. In an embodiment,the inputs used to derive the baseline interference scenario can includetypical inputs such as those suggested in the 3GPP TS 25.967, andadditional inputs as suggested in this document, or both. The RAN/RFparameters adjusted can include typical inputs such as those suggestedin the 3GPP TS 25.967, and additional RAN/RF parameters as suggested inthis document, or a combination thereof In one embodiment, step 510 canbe performed by the assessment module 330.

In step 512, a determination is made in real-time whether any of thefactors influencing the interference scenario and the RAN/RF parametersthat represent the current state of the RF network and the systemenvironment have changed. If these factors have not changed, thisparallel activity continues with the method proceeding to step 530. Ifthe factors have changed, the method proceeds to step 514 where thebaseline interference and RAN/RF parameters are modified to account forthe observed changes, and the method proceeds to decision step 530. Inone embodiment, step 512 can be performed by the assessment module 330,and step 514 can be performed by the control response module 340.

The process of managing the influence on classes of service andindividual connections, and conversely, managing the influence ofindividual services and their associated class of service on theinterface environment can be begun in parallel with step 510. In step520, the maximum or target bit error rate (BER) or packet error rate(PER) (or other quality metric) is established for each class of serviceor each individual service or connection. Each individual service orconnection's actual BER, PER, or other quality metric can be monitored.The maximum or target BER and PER values can be determined based on theoperator policy information 310 provided by the network provider.Additionally, in step 520, the throughput needs or targets of theservice can also be determined. These throughput targets can havemultiple levels, corresponding to multiple levels of QoS that requirediffering levels of throughput. The throughput targets can also takeinto account expected retransmissions based on knowledge of theapplications or the transport mechanisms used at the various layers ofcommunication protocol. In one embodiment, step 520 can be performed bythe control set point modules 315.

In step 522, a determination is made whether the actual error rates,such as the BER or PER, or other actual quality metric exceeds a targetthreshold for the connection determined in step 510. If the BER or otherquality metric exceeds the threshold for the connection, the methodproceeds to decision step 524 to start the process of taking correctiveaction. Otherwise, if the quality metric are no worse than the target,the method proceeds to decision step 530. In one embodiment, step 522can be performed by the assessment module 330.

In step 524, a determination is made whether it is acceptable for theaffected service provider to operate in a manner that can exceed thebaseline interference scenario and baseline RAN/RF parameters, whichcould cause greater interference to services active in neighboringcells. For example, a temporary slight increase in transmission power(e.g., 0.5 dB) can add a tolerable increase in interference to servicesin neighboring cells. If it is acceptable for the affected serviceprovider to operate in manner that can exceed the baseline interferencescenario and baseline RAN/RF parameters, the method proceeds to step 514where the baseline interference scenario and RAN/RF parameters can betemporarily adjusted to accommodate the need for improved QoS for theservice. According to an embodiment, this adjustment may be allowedsolely for the affected service or connection, or can be allowedgenerally for the cell. In one embodiment, step 524 can be performed bythe assessment module 330 and/or the control response module 340.

If in decision step 524 a determination is made that the baselineinterference scenario cannot be exceeded, the method proceeds to step526 where the transmission parameters of the service are modified toachieve the target BER/PER or quality metric without violating thecurrent baseline interference scenario. In an embodiment, this caninclude changes in modulation and coding, transmit power or any of theother adjustable transmission parameters. In one embodiment, step 526can be performed by the control response module 340.

According to an embodiment, when parameters are adjusted, there is apossibility that the bandwidth requirements to meet demand can exceedthe current available aggregate throughput of the cell. Hence, bothparallel paths of the method proceed to decision step 530, where adetermination is made as to whether the demand exceeds the currentavailable aggregate throughput. If the current available aggregatethroughput of the cell is not exceeded, the method returns to step 501and can continuously repeat. Otherwise, the method continues to step 540before continuing to step 501 to repeat. In step 540, a method tomitigate oversubscription is selected and applied. Several methods formitigating oversubscription are described below. In one embodiment,steps 530 and 540 can be performed by the control response module 340.

According to an embodiment, the method illustrated in FIG. 5 can includean uplink instance and a downlink instance that operate independently,for example in a Frequency Division Duplex (FDD) system. Conversely, inother embodiments, the uplink and downlink instances may need to shareinformation in a Time Division Duplex (TDD) system where the uplink anddownlink are on the same frequency and may, therefore, contributeinterference in certain situations. This may be especially true of TDDsystems that adapt the uplink/downlink ratio dynamically.

According to an embodiment, the optimization module can also implementanother method to optimize performance based on historical data toperform anticipated adaptation to reduce potential oversubscription.According to an embodiment, the optimization module can implement thissecond method, which can be used to update the operator policy 310. Ahistory of interference can be built through sensing and/or through theuse of shared metrics received from other network elements (e.g., thecore network, BTSs, UEs). The interference data can be grouped by dateand/or time in order to build a picture of interference patterns forvarious time frames. For example, the interference data can be groupedby the time of day, the day of the week, or by marking the data asholiday or non-holiday. The sensing and/or shared metrics can alsoinclude traffic metrics for the SFF base station's own cell and/or forneighboring cells. The can also include “update with memory trail off”where weighted averaging, exponential averaging, or some other method isused to give higher importance to more recent data.

Preemptive decisions can be made based on the history of interferencethat has been built. For example, a determination can be made whethermore or less strict CAC, policy, and/or power control may help to reducethe likelihood of oversubscription. In an embodiment, a determinationcan be made whether trading off likely robustness versus BER/PER.

According to an embodiment, real time monitoring based on the firstmethod described above and illustrated in FIG. 5 can be used in caseunexpected usage patterns disrupt the predictive interference methoddescribed in the second method. In an embodiment, predictive data can beused for a baseline scenario and the first method can be used forreal-time optimization of the system. In another embodiment, predictivedata generated using the second method can be used to update theoperator policy 310, and the first method can be used to apply theupdated policy.

Intelligent Discard

Referring to FIG. 5, intelligent discard can be used as one of thetechniques of algorithm step 540 to mitigate oversubscription caused bymodification of transmission parameters in step 526 or caused by varyingthe interference scenario and RAN/RF parameters in step 514. This is thereactive form of intelligent discard. Alternatively, knowledge ofavailable intelligent discard techniques may be used to influence thethroughput level target in step 520, the transmission parametermodifications in step 526, and the changes to the interference scenarioand RAN/RF parameters in step 514. This is the interactive form ofintelligent discard. The interactive form may further be made proactiveby using other system information to predict the future oversubscriptionof bandwidth.

According to an embodiment, intelligent discard can be practiced by anyentity of the communications network that performs scheduling. This caninclude the scheduling of downlink bandwidth by any form of base stationincluding macrocell, picocell, enterprise femtocell, residentialfemtocell, relays, or any other form of scheduling. Intelligent discardcan be performed by any form of device that transmits in the uplinkdirection, including user devices, both fixed and mobile, and relaydevices. In an embodiment, intelligent discard can be performed by ascheduling algorithm that is implemented in the core network, whichcentrally directs the actions of devices. In another embodiment,intelligent discard can also be predictively performed by an entity,such as a base station, that allocates uplink bandwidth for use byanother entity, such as a user device capable of intelligent discard.The base station and the user device can negotiate whether or not theuser device has intelligent discard capability or it may be known basedon the model identification of the user device. According to anembodiment, this approach where an entity, such as a base station, thatallocates bandwidth for use by another entity in the network capable ofintelligent discard, can coordinate with the other entity, such as auser device, can be referred to as cooperative intelligent discard.

Reactive Intelligent Discard

In step 530 of FIG. 5, a determination is made whether or not theapplication layer throughput demand for bandwidth currently exceeds theavailable aggregate throughput or whether a specific session orconnection is exceeding its allocated throughput. For instance, in step520, throughput level targets can be established for the activeconnections being serviced by the base station in question. These targetlevels can be expressed in such quantitative terms as bits per second orbytes per second. In an embodiment, these target levels can includeallowances for retransmissions. Based upon the transmission parametersselected in step 526 and the RAN/RF parameters selected in steps 510 and514, the throughput levels can be translated into required physicallayer resources, such as the resource blocks used in 3GPP LTE, QAMsymbols, OFDM symbols, subchannels, UL/DL ratio, or combinationsthereof. The required physical layer resources can include allowancesfor HARQ or other retransmissions. Once converted to physical layerresources, the throughput level targets or demand can be comparedagainst available physical layer resources as is indicated in step 530.This comparison may return a result indicting that demand for physicalresources currently exceeds available physical resources. In this case,a reduction in physical resource demand is necessary in order to notexceed available physical resources. This in turn determines a necessaryreduction in the current demand for bandwidth at the session, connectionand/or application.

According to an alternative embodiment, other algorithms can be used todetermine whether the demand for physical resource exceeds the availablephysical resources which can provide an available throughput metric thatcan be used for reactive intelligent discard,

Once a determination is made that application layer throughput demandexceeds available physical resources, intelligent discard can be used instep 540 to reduce the demand while minimizing the need to curtailindividual services and while maximizing the quality perceived by theend user.

For instance, if the demand for resources for a VoIP service exceeds theavailable physical resources by 10%, random (not intelligent) discardmay cause consecutive or near consecutive VoIP packets to be discarded.In contrast, reactive intelligent discard can identify a number packetsthat can be dropped in order to reduce at least a portion of the excessdemand for bandwidth while preserving the perceived quality of the call.For example, in one embodiment, in an intelligent discard system, thescheduler can discard every tenth packet. This could include packetsalready queued by the scheduler, or packets as they are being queued, orboth. The even distribution of discarded packets by the intelligentdiscard algorithm may be less noticeable to the end user than clumpingof discarded packets by a random discard algorithm. According to anembodiment, other patterns can be used to select the packets to bediscarded, so long as the selected pattern minimizes the number ofconsecutive and near consecutive packets that are discarded.

According to an embodiment, the discard algorithm can also be adjusteddepending on the specific voice protocol and codec being used.Intelligent discard can allow the call to continue with acceptablequality, as determined by a quality score and compared to the operator,system, or local policy.

In another example, in MPEG-2 transmissions, audio packets are moreimportant than video packets, because humans notice changes in audioquality in MPEG-2 transmissions more readily than they notice changes invideo quality. Additionally, the video packets are comprised ofintra-coded frames (“I-frames”), predictive-coded frames (“P-frames”),and bidirectionally-predictive-coded frames (“B-frames”). The loss of anI-frame is typically more detrimental to the quality of an MPEG-2transmission than the loss of a P-frame or B-frame. In fact, the loss ofan I-frame can result in the receiving device being unable to use aP-frame, even if the P-frame is received correctly. So, in MPEG-2intelligent discard may discard P-frames and B-frames preferentially toI-frames and may discard all forms of video frames preferentially toaudio frames.

For MPEG-4 transmission, in addition to the distinction between framesinherited from MPEG-2, there are 11 levels of spatial scalability, 3levels of temporal scalability, and a variable number of levels ofquality scalability depending upon the video application. Fine grainscalability combines these into 11 levels of scalability. In anembodiment, “marking” of packets with information can be performed andthe markings can be used by intelligent discard to allow a fine grainedvarying of quality as available physical resources change.

As with the VoIP example, in the MPEG examples, intelligent discard canperform discard of already queued packets as well as discard upon entryto the scheduling queue. The intelligent discard of a percentage ofpackets can allow more services to be maintained and accepted by thesystem's call admission control (CAC) algorithms.

In step 540, there may be more than one choice of service that can haveintelligent discard applied to meet the physical layer resourceconstraints. There are numerous criteria that can be used to choose theservice or services to which to apply intelligent discard. For instance,intelligent discard can be applied in a round robin fashion, similarlyimpacting all services or all services within a selected class or set ofservices. Intelligent discard can be applied based on the identity ofthe end user or membership of the end user in some group. For instance,different users may pay more or less for different service levelagreements with the operator of the network. Users with a lower levelagreement may be impacted preferentially to users with a higher levelagreement. Users that are roaming from another network may be impactedby intelligent discard preferentially to users that subscribe directlyto the network. The decision can be based on service type orapplication. For instance, a VoIP call being made via a third partyapplication such as Skype may be impacted preferentially to a VoIP callmade via a VoIP service directly provided by the operator. Which serviceto impact can be determined algorithmically to maximize totalthroughput. The decision on how to apply intelligent discard is based onsystem, operator, or autonomous policy. For instance, a device may havea default policy which may be modified or overridden by a system oroperator policy.

The decision as to which services to impact can be based on relativedegradation, impacting first, for example, those service whose observedquality is least impacted by intelligent discard regardless of therelative quantity of discarded data. To facilitate this, step 540 cancalculate a score for each of the possible throughput levels for thevarious services. These scores represent a relative level of observedquality for each throughput level. These scores may be based onsubjective criteria, such as MOS scores used to score voice quality, ormay be quantitative such as the elimination of a feature from theservice. The scores can be used in step 540 as part of the determinationof which service will have intelligent discard applied and to whatextent. For example, once a set of scores for a set of possiblethroughput levels for services requiring bandwidth, a target bandwidthlevel can be selected for one or more of the services based on the setof scores calculated for the various throughput levels, and packetsassociated with each service can be selectively discarded to reduce thethroughput associated with each of the services to the target throughputlevel associated with that service.

Reactive intelligent discard can be performed in any portion of thesystem that can make a choice regarding transmission or disposition of apacket. For instance, in one embodiment, a base station, pico station,femto station or relay station can include a transceiver fortransmitting and receiving packets. According to a preferred embodiment,these stations can include a medium access control (MAC) layerresponsible for allocation of bandwidth on the uplink and/or thedownlink. The MAC layer preferably can contain or be associated with ascheduler and buffers for storing packets prior to transmission. In oneembodiment, the intelligent discard techniques disclosed herein can beimplemented in the portion of the MAC layer responsible for buffering adscheduling the transmission of packets. Alternatively, the equivalent ofthe MAC scheduler can reside in a core network element that performscentralized scheduling, and possibly, buffering. For example, in oneembodiment, the equivalent of the MAC scheduler could be implemented tocoordinate simultaneous transmission of data, such as broadcast video oraudio, on two or more base stations or other similar devices.

In an embodiment, the intelligent discard techniques can also beimplemented in the MAC scheduler of a user device that schedules andbuffers data prior to transmission in the uplink. According to anembodiment, the core network or base station (or equivalent device) canbe configured to mark packets prior to buffering to facilitate makingeasier discard decisions in the downlink direction. Alternatively, afunction preceding the buffering of packets for uplink transmission bythe user device can mark packets for easier discard decisions by the MACscheduler function in the user device.

Interactive Intelligent Discard

In addition to the previously described reactive intelligent discard,the intelligent discard algorithm can interact with other aspects of thesystem control to gain improved performance. For example, referring nowto FIG. 5, in one embodiment changing a particular RAN/RF networkoperating parameter, such as lowering the maximum transmit power in step510, might benefit neighboring cells by reducing the observedinterference of those cells.

Alternatively, choosing a more robust modulation scheme in step 526 canalso have a similar effect. In a typical system, these changes could beundesirable due to the resulting decrease in available physicalresources, causing the application layer throughput demand to exceedavailable bandwidth. In contrast, in a system employing interactiveintelligent discard, in step 520, a set of throughput levels can becalculated for the active services. The set of throughput levelsrepresents a larger range of physical resource demands when the possibletransmission parameter choices of step 526 and possible RAN/RFparameters of step 510 are considered. Knowledge of these possiblecombinations of quality levels, transmission, and RAN/RF parametersallows the system in steps 510 and 526 to choose parameters that cansubstantially increase robustness of the system, temporarily orpermanently, at the sacrifice of a small amount of quality to one ormore services.

Alternative Implementation of Interactive Intelligent Discard

FIG. 6 is a flow diagram of a modified version of the method illustratedin FIG. 5 that enables other aspects of network operation, such asinterference mitigation and power control, to make use of intelligentdiscard to further optimize system performance. In step 620, rather thancreating a single quality (e.g., BER or PER) and throughput level for aservice or connection (as in step 520 of FIG. 5), a set of throughputlevels and/or range of quantitative quality thresholds (e.g., BER andPER) can be created (605). A score can be applied to each of thethroughput levels. The score represents a relative level of observedquality for each throughput level. According to an embodiment, a scorecan be applied to each of the throughput levels to indicate a relativelevel of observed quality for each throughput level. The scores can bebased on subjective criteria, such as MOS scores used to score voicequality, or the scores can be quantitative, such as the elimination of afeature from the service. The scores can be used in step 640 as part ofthe determination of which server will have intelligent discard appliedand to what extent.

The set of throughput levels and scores, exemplified by data block 605,can be used by step 610, decision step 612, and modified step 614 tomake tradeoffs between service quality and other system operationalfactors. Other steps, such as step 626 can also use the set ofthroughput levels and scores to optimize performance choices. Forinstance, based on the throughput levels and scores, the method in step610 can choose to apply a more robust modulation and lower power thebaseline parameters for a service, with the knowledge that theperformance degradation to the individual service will be small relativeto the reduction in interference caused to neighboring cells. In fact,the change in RAN/RF parameters can be a reaction to a request forinterference reduction from a neighboring cell, or a command or requestfor interference reduction or noise floor reduction from a networkmanagement entity or other centrally located control function, or anautonomous decision to reduce power, interference potential, or someother aspect of network operation. In this way, step 610 and similarfunctions can assess the quality impact implied by the throughput impactresulting from potential alternative actions that can be applied to thepreviously independent task of choosing appropriate RAN/RF parameters.

In a preferred embodiment, an interactive intelligent discard methodimplements the discard function in the equivalent of the MAC layerscheduler and packet buffering capability prior to transmission by thetransceiver of the station, user device, or network functionimplementing interactive intelligent discard. The derivation of sets ofquality thresholds, throughput levels, and scores can be performed by afunction that can be implemented in the core network, the base station(macro, pico or femto), or user devices and provides the information tothe interactive intelligent discard function which interacts with thebuffering and scheduling in the MAC layer to perform intelligentdiscard. The interactive intelligent discard function can also interactwith the physical layer functions, which monitor the RF environment, andinteracts with core network functions or functions on other basestations or network elements to exchange information about the RFenvironments of neighboring cells. A network facing function withininteractive intelligent discard can provide information regarding theservices, user devices, and RF environment to a core network function orto an interactive intelligent discard function on neighboring devices.The interactive intelligent discard method can provide information to anRF or Physical Layer (PHY) control module, which adjusts the RAN/RFparameters for the transmission of certain information packets.

Proactive Intelligent Discard

According to an embodiment, proactive intelligent discard is a techniquefor predictively performing intelligent discard in anticipation ofoversubscription conditions and for performing the discard before theoversubscription conditions actually occur. Proactive intelligentdiscard can be used to reduce anticipated demand when the anticipateddemand for network bandwidth exceeds anticipated available bandwidth.

Proactive intelligent discard may be applied reactively. For example,expectation of a handover creates expectation of more robust modulationand, therefore, lower throughput per physical layer resource unit as amobile station approaches the edge of a cell. Proactive intelligentdiscard can be used to discard ahead of the actual event, allowingsmoother handovers with controlled discard of data rather than randomloss of data due to congestion.

Proactive intelligent discard can be applied interactively. Forinstance, it may be known from historical data that interference to orfrom neighboring cells increases at a certain time of day (dailycommute, etc.). In proactive intelligent discard, step 612 can determinethat the factors influencing the RAN/RF parameters are about to change,and in step 614 the RAN/RF parameters can be modified based on theassumption that the change will be needed in combination with the set ofthroughput levels and scores created by step 620 in order to proactivelymodify the system parameters so that intelligent discard can preserveoptimal throughput and quality based on the systems policies regardingquality and throughput.

Proactive intelligent discard may be performed based on a variety ofstimuli or trigger events. Some examples of the types of stimuli ortrigger events that can be used to trigger the execution of proactiveintelligent discard include:

(1) Motion—if it is determined that the device is not stationary or isexceeding some speed threshold, proactive intelligent discard mayanticipate the need to perform intelligent discard based on expectationsof motion caused changes in physical parameters that impact throughputavailability.

(2) Expectation of handover—if it is determined that the likelihood ofhandover exceeds some threshold metric, intelligent discard canproactively discard data in a controlled fashion so as to minimize thequality impact of predicted decrease in resources.

(3) Time of day, day of week, or other historical patterns—historicaldata may show that decrease in resources may be expected at predictablepoints in time. Proactive intelligent discard can prepare the system forsmooth transition to lower resources.

(4) Active/inactive user devices in a cell—The number of user devices ina cell may be used to predict fluctuations in demand that would causereactive intelligent discard to take action.

(5) Reserve resources—proactive intelligent discard can aid in servicequality preservation by proactively performing intelligent discard tokeep resources in reserve for other functions such as Call AdmissionControl which may be able to serve more active calls if intelligentdiscard is applied

(6) Changes to Neighbor Cells—information regarding changes in thequantity and configuration of neighboring cells, including but notlimited to: number of neighbor cells, location of neighbor cells, CellOperator, frequency and bandwidth of operation, number of active/idleUEs, RF/RAN parameters.

Additionally, proactive intelligent discard can provide a smoothertransition from one level of discard to another, minimizing the impacton quality of service parameters such as jitter and individual packetdelay.

In an embodiment, proactive intelligent discard can also be used in animplementation where the discard occurs before being needed, applying alower throughput in anticipation of lack of resources. In an alternativeembodiment, proactive intelligent discard can be used in animplementation where the packets to be dropped during the period ofexpected lack of resources are tagged for quick discard, but onlydiscarded in the event that the anticipated lack of resources actuallyoccurs.

In an embodiment, the intelligent discard can also perform the inverserole: accelerating packet transmission into the channel before acapacity limitation comes into effect. This may allow the avoidance of afuture short-term resource constraint.

The historical or other data that is used to create the patterns orhistory that is used to proactively implement intelligent discard cancome from a variety of sources. For example, RF modules can collectinformation regarding the physical environment. In another example, theMAC layer can collect information regarding packet demand andthroughput, and numbers of active or inactive user devices and services.In one embodiment, the information can be processed locally on a deviceto convert the inputs into historical trends, or in an alternativeembodiment, the information can be forwarded to a function in the corenetwork or any other processor for conversion into historical trends andpatterns. The historical trends and patterns can be used locally by adevice or may be shared between devices, such as in the case whereinteractive intelligent discard is applied proactively.

Those of skill will appreciate that the various illustrative logicalblocks, modules, units, and algorithm steps described in connection withthe embodiments disclosed herein can often be implemented as electronichardware, computer software, or combinations of both. To clearlyillustrate this interchangeability of hardware and software, variousillustrative components, units, blocks, modules, and steps have beendescribed above generally in terms of their functionality. Whether suchfunctionality is implemented as hardware or software depends upon theparticular system and design constraints imposed on the overall system.Skilled persons can implement the described functionality in varyingways for each particular system, but such implementation decisionsshould not be interpreted as causing a departure from the scope of theinvention. In addition, the grouping of functions within a unit, module,block or step is for ease of description. Specific functions or stepscan be moved from one unit, module or block without departing from theinvention.

The various illustrative logical blocks, units, steps and modulesdescribed in connection with the embodiments disclosed herein can beimplemented or performed with a general purpose processor, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general-purpose processor can be a microprocessor,but in the alternative, the processor can be any processor, controller,microcontroller, or state machine. A processor can also be implementedas a combination of computing devices, for example, a combination of aDSP and a microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration.

The steps of a method or algorithm and the processes of a block ormodule described in connection with the embodiments disclosed herein canbe embodied directly in hardware, in a software module (or unit)executed by a processor, or in a combination of the two. A softwaremodule can reside in RAM memory, flash memory, ROM memory, EPROM memory,EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or anyother form of machine or computer readable storage medium. An exemplarystorage medium can be coupled to the processor such that the processorcan read information from, and write information to, the storage medium.In the alternative, the storage medium can be integral to the processor.The processor and the storage medium can reside in an ASIC.

Various embodiments may also be implemented primarily in hardware using,for example, components such as application specific integrated circuits(“ASICs”), or field programmable gate arrays (“FPGAs”). Implementationof a hardware state machine capable of performing the functionsdescribed herein will also be apparent to those skilled in the relevantart. Various embodiments may also be implemented using a combination ofboth hardware and software.

The above description of the disclosed embodiments is provided to enableany person skilled in the art to make or use the invention. Variousmodifications to these embodiments will be readily apparent to thoseskilled in the art, and the generic principles described herein can beapplied to other embodiments without departing from the spirit or scopeof the invention. Thus, it is to be understood that the description anddrawings presented herein represent a presently preferred embodiment ofthe invention and are therefore representative of the subject matter,which is broadly contemplated by the present invention. It is furtherunderstood that the scope of the present invention fully encompassesother embodiments that may become obvious to those skilled in the art.

1. A multivariate control system for managing bandwidth capacity in acapacity and spectrum constrained, multiple-access communicationnetwork, the system comprising: a policy parameters module configured toreceive policy parameters that define operational requirements for thecommunication network; an environment parameters module configured toreceive environment parameters that represent real-time informationdescribing the operating status of the communication network; a controlset points module in communication with the policy parameters module andconfigured to receive policy parameters from the policy parametersmodule and to generate a set of control set points used to assesswhether current operating status of the communication network meets theoperational requirements defined in the policy parameters; a real-timeprofile module in communication with the environment parameters moduleand configured to receive environment parameters from the environmentparameters module and to generate a real-time profile of thecommunication network that represents current operating conditions ofthe communication network; an assessment module in communication withthe real-time profile module and the control set point module andconfigured to receive the real-time profile from the real-time profilemodule and the set of control set points from the control set pointmodule, and to determine whether the current operating conditions of thecommunication network meet the operational requirements in the policyparameters, and to generate feedback signals indicating that the currentoperating conditions of the communication network do not meet theoperational requirement; and a control response module in communicationwith the assessment module and configured to receive the feedbacksignals from the assessment module and to generate control signals forone or more components of the communication system to adjust theoperating parameters of the one or more components of the communicationsystem.
 2. The control system of claim 1 wherein the policy parameterscomprise quality of service parameters and interference parameters. 3.The control system of claim 1 wherein the policy parameters comprisequality of service parameters.
 4. The control system of claim 1 whereinthe policy parameters comprise interference parameters.
 5. The controlsystem of claim 1 further comprising: a patterns module in communicationwith the real-time profile module, the environment parameters module,and the control response module, the patterns module being configured toreceive the real-time profile from the real-time profile module and theenvironment parameters from the environment parameters module, and togenerate patterns that comprise a set of historical quantitativeparameter patterns based on the real-time profile and environmentparameters.
 6. The control system of claim 5 wherein the patterns moduleis configured to generate feedforward signals based on the patterns, andwherein the control response module is configured to receive thefeedforward signals and to generate control signals for one or morecomponents of the communication system to adjust the operatingparameters of the one or more components of the communication system. 7.The control system of claim 1 wherein the control system is implementeddistributed between a base station and other core network elements. 8.The control system of claim 1 wherein the control system is implementedin user equipment.
 9. The control system of claim 1 wherein the controlsystem is implemented in a base station.
 10. The control system of claim9 wherein the base station is a picocell.
 11. The control system ofclaim 9 wherein the base station is a femtocell.
 12. The control systemof claim 9 wherein the base station is a macrocell.
 13. The controlsystem of claim 9 wherein the control response module is configured togenerate a control signal to instruct the base station to discardnetwork packets to reduce the demand for bandwidth if the currentoperating conditions of the communication network do not meet theoperational requirements.
 14. The control system of claim 13 wherein theassessment module is configured to determine whether an actual errorrate exceeds a target error rate threshold, the target error rate beingderived from the operating policy and the actual error rate beingdetermined from the real-time profile.
 15. The control system of claim13 wherein if the actual error rate exceeds the target error ratethreshold, the assessment module is configured to determine whether thebase station can adjust the operating parameters of the base station toexceed a set of baseline interference parameters in order to reduce theactual error rate below the target error rate threshold.
 16. The controlsystem of claim 15 wherein the assessment module is configured togenerate a feedback signal instructing the control response module togenerate a control signal instructing the base station to adjust thetransmission parameters to reduce the actual error rate below the targeterror rate threshold if the base station cannot adjust the operatingparameters of the base station to exceed a set of baseline interferenceparameters.
 17. The control system of claim 16 where the assessmentmodule is configured to determine whether current available bandwidthhas decreased below current demand for bandwidth as a result ofadjusting the transmission parameters, and wherein the assessment moduleis configured to generate a feedback signal instructing the controlresponse module to generate a control signal instructing the basestation to selectively discard packets in order to decrease the currentdemand for bandwidth.
 18. The control system of claim 15 wherein theassessment module is configured to generate a feedback signalinstructing the control response module to generate a control signalinstructing the base station to modify baseline network operatingparameters, if the base station can adjust the operating parameters ofthe base station to exceed a set of baseline interference parameters.19. The control system of claim 18, wherein the base station is a firstbase station of at least two base stations, if the first base stationcan adjust the operating parameters of the first base station to exceedthe set of baseline interference parameters, wherein exceeding thebaseline interference pattern causes the throughput of a second basestation to decrease, and wherein the second base station is configuredto mitigate over subscription caused by modifying the operatingparameters of the first base station by selectively discarding packets.20. The control system of claim 18, wherein if the base station canadjust the operating parameters of the base station to exceed the set ofbaseline interference parameters, wherein the base station is configuredto allocate bandwidth to a device, wherein the device is configured tomitigate oversubscription by selectively discarding packets, wherein thebase station is configured to coordinate with the device to selectivelydiscard packets to mitigate oversubscription, and wherein the basestation is configured to adjust the operating parameters to exceed theset of baseline interference parameters.
 21. The control system of claim13 wherein the control response module is configured to use reactiveintelligent discard to mitigate oversubscription.
 22. The control systemof claim 21 wherein the control response module is configured to.identify a level of quality of service importance associated withpackets of network traffic; and selectively discard packets having alower level of quality of service importance associated with thepackets.
 23. The control system of claim 22 wherein the control responsemodule is further configured to: identify a set of services requiring anallocation of bandwidth; identify services for which selectivelydiscarding packets would result in a least amount of relativedegradation of quality of service if the packets were discarded; anddiscard packets from the identified services to reduce the demand forbandwidth.
 24. The control system of claim 23 wherein the controlresponse module when identifying services for which selectivelydiscarding packets would result in the least amount of relativedegradation of quality of service if packets were discarded, is furtherconfigured to: calculate a set of scores for a set of possiblethroughput levels for each service, each score representing a relativelevel of observed quality for each throughput level; select a targetthroughput level for one or more services based on the set of scoresassociated with the set of throughput levels associated with eachservice; and discard packets associated with the one more services sothat a throughput level associated with the service reaches the targetthroughput level.
 25. The control system of claim 13 wherein the controlresponse module is configured to use proactive intelligent discard tomitigate predicted oversubscription.
 26. The control system of claim 25wherein the control response module is configured to discard packets tomitigate potential oversubscription in response to one or more networkevents that are likely to decrease network throughput.
 27. The controlsystem of claim 26 wherein the one or more network events are likely todecrease network throughput due to interference from one or moreneighboring base stations or based on interference to the one or moreneighboring base stations.
 28. The control system of claim 25 whereinexecuting the proactive intelligent discard method is conditioned on theoccurrence of a trigger event.
 29. The control system of claim 28wherein the trigger event for executing the proactive intelligentdiscard is a determination that a user device is motion and is exceedinga predetermined speed threshold, and wherein the control response moduleis configured to discard packets associated with the user device inresponse to the determination.
 30. The control system of claim 28wherein the control system is configured to determine the likelihoodthat a particular user device will be handed off to another basestation; and wherein the control response module is configured totrigger the selective discard of packets associated with the user deviceif the likelihood that the user device will be handed off to anotherbase station exceeds a predetermined threshold.
 31. The control systemof claim 28 wherein the discard is triggered at a predetermined date andtime.
 32. The control system of claim 28 wherein the trigger eventcomprises identification of a number of active user devices within acell associated with the base station, and wherein control responsemodule is configure to discard packets if the number of active userdevices exceeds a predetermined threshold.
 33. The control system ofclaim 28 wherein the control response module is configured to: monitorusage and available resources to determine if available resources havefallen below a predetermined threshold; and if available resources havefallen below a predetermined threshold, trigger selective proactivediscard of packets to decrease the usage of resources to increase theavailable resources in reserve for other services.
 34. The controlsystem of claim 25 wherein the trigger event comprises changes to theoperating parameters of one or more neighboring cells.
 35. The controlsystem of claim 25 wherein the control response module is configured to:select packets to be discarded in the event that an anticipatedshortfall of resources occurs; tag the selected packets for deletion inthe event the shortfall occurs; and discard the selected packets only ifthe anticipated shortfall occurs.
 36. The control system of claim 21wherein the control response module is configured to increase a packettransmission rate in an attempt to avoid an anticipated shortfall ofresources.
 37. A multivariate control system for managing bandwidthcapacity in a capacity and spectrum constrained, multiple-accesscommunication network, the system comprising: an environment parametersmodule configured to receive environment parameters that representreal-time information describing the operating status of thecommunication network including current available bandwidth and currentdemand for bandwidth; a real-time profile module in communication withthe environment parameters module and configured to receive environmentparameters from the environment parameters module and to generate areal-time profile of the communication network that represents thecurrent demand for bandwidth and current available bandwidth on thecommunication network; an assessment module in communication with thereal-time profile module and the control set point module and configuredto receive the real-time profile from the real-time profile module andthe set of control set points from the control set point module,determine whether the current demand for bandwidth exceeds the currentavailable bandwidth based on the real-time profile, and generatefeedback signals indicating that the control response module shoulddiscard network packets to mitigate oversubscription based on the policyparameters; and a control response module in communication with theassessment module and configured to receive the feedback signals fromthe assessment module and to selectively discard packets to reduce thecurrent demand for bandwidth.
 38. The control system of claim 37 whereinthe control response module is configured to. identify a level ofquality of service importance associated with packets of networktraffic; and selectively discard packets having a lower level of qualityof service importance associated with the packets.
 39. The controlsystem of claim 38 wherein the control response module is furtherconfigured to: identify a set of services requiring an allocation ofbandwidth; identify services for which selectively discarding packetswould result in a least amount of relative degradation of quality ofservice if the packets were discarded; and discard packets from theidentified services to reduce the demand for bandwidth.
 40. The controlsystem of claim 39 wherein the control response module when identifyingservices for which selectively discarding packets would result in theleast amount of relative degradation of quality of service if packetswere discarded, is further configured to: calculate a set of scores fora set of possible throughput levels for each service, each scorerepresenting a relative level of observed quality for each throughputlevel; select a target throughput level for one or more services basedon the set of scores associated with the set of throughput levelsassociated with each service; and discard packets associated with theone more services so that a throughput level associated with the servicereaches the target throughput level.
 41. The control system of claim 37wherein the control response module is configured to use proactiveintelligent discard to mitigate predicted oversubscription.
 42. Thecontrol system of claim 41 wherein the control response module isconfigured to discard packets to mitigate potential oversubscription inresponse to one or more network events that are likely to decreasenetwork throughput.
 43. The control system of claim 42 wherein the oneor more network events are likely to decrease network throughput due tointerference from one or more neighboring base stations or based oninterference to the one or more neighboring base stations.
 44. Thecontrol system of claim 41 wherein executing the proactive intelligentdiscard method is conditioned on the occurrence of a trigger event. 45.The control system of claim 41 wherein the control response module isconfigured to: select packets to be discarded in the event that ananticipated shortfall of resources occurs; tag the selected packets fordeletion in the event the shortfall occurs; and discard the selectedpackets only if the anticipated shortfall occurs.
 46. The control systemof claim 37 wherein the control response module is configured toincrease a packet transmission rate in an attempt to avoid ananticipated shortfall of resources.